Blood neurofilament light chain as a predictive biomarker for functional outcome of acute ischemic stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
Aim: Ischemic stroke continues to be a significant contributor to mortality and disability on a global scale. The blood neurofilament light chain (bNfL) as a prognostic indicator for stroke functional outcomes is a topic of ongoing debate. Thus, the objective of this systematic review is to assess the efficacy of bNfL as a predictor of stroke functional outcomes. Materials & methods: A systematic search was conducted in Pubmed, Cochrane and Embase databases from their inception to 21 October 2023. Two reviewers independently screened the search results to identify studies reporting on the association between bNfL and acute ischemic stroke outcomes. The quality of the studies was assessed using the Newcastle–Ottawa scale. Meta-analysis was conducted using the Comprehensive Meta-Analysis software Stata 12.0, utilizing a random effects model to estimate the pooled effect. Results: Nine studies involving 2302 patients were included in the analysis. A pooled analysis of adjusted odds ratios (ORs) from multivariate regression models in the meta-analysis revealed a pooled adjusted OR of 1.929 [95% CI:1.459, 2.550], suggesting that the patients with higher bNfL levels are at a greater risk of experiencing unfavorable functional outcomes compared with those with lower bNfL levels. Subgroup analysis indicated that factors such as sampling time, study region, participant age, blood specimen and sample size, may contributed to high heterogeneity in the results. After conducting a thorough analysis using funnel plot and Egger’s test, no significant evidence of publication bias was found in our study. Conclusion: In summary, bNfL demonstrates potential as a predictive biomarker for functional outcomes in acute ischemic stroke patients, albeit subject to influence from confounding variables. Additional rigorously designed and meticulously executed prospective studies on a larger scale are warranted to validate these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".